![\"\"](\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2026\/09\/20260908142619-6aa01b0bdf62a.png\")
<\/h3>\n<\/p>\n<\/h3>\n<\/p>\n\u968f\u7740\u7aef\u4fa7 AI \u9700\u6c42\u7206\u53d1，\u5728\u8d44\u6e90\u53d7\u9650\u7684 MCU \u4e0a\u90e8\u7f72\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\u5df2\u6210\u4e3a\u5d4c\u5165\u5f0f\u5f00\u53d1\u7684\u4e3b\u6d41\u8d8b\u52bf\u3002TFLite Micro \u4f5c\u4e3a\u8c37\u6b4c\u63a8\u51fa\u7684\u8f7b\u91cf\u7ea7\u63a8\u7406\u6846\u67b6，\u662f\u76ee\u524d TinyML \u9886\u57df\u5e94\u7528\u6700\u5e7f\u6cdb\u7684\u65b9\u6848\u4e4b\u4e00，\u4f46\u5b9e\u9645\u843d\u5730\u8fc7\u7a0b\u4e2d\u5f00\u53d1\u8005\u666e\u904d\u9762\u4e34\u4e09\u5927\u75db\u70b9：<\/p>\n<\/p>\n
\u672c\u6587\u63d0\u4f9b\u7684\u5f00\u7bb1\u5373\u7528\u65b9\u6848，\u5c06\u4e0a\u8ff0\u73af\u8282\u5168\u90e8\u5c01\u88c5\u4e3a\u6807\u51c6\u5316\u7ec4\u4ef6，\u5f00\u53d1\u8005\u53ea\u9700\u66ff\u6362\u6a21\u578b\u6587\u4ef6\u5373\u53ef\u5feb\u901f\u90e8\u7f72，\u5927\u5e45\u964d\u4f4e\u7aef\u4fa7 AI \u843d\u5730\u95e8\u69db\u3002<\/p>\n<\/p>\n
![\"\"](\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2026\/09\/20260908142621-6aa01b0db0d8c.png\")
<\/p>\n
\u6211\u4eec\u8bbe\u8ba1\u7684\u63a8\u7406\u9aa8\u67b6\u91c7\u7528\u5206\u5c42\u67b6\u6784，\u5b8c\u5168\u5c4f\u853d\u5e95\u5c42\u786c\u4ef6\u548c\u6846\u67b6\u7ec6\u8282：<\/p>\n<\/p>\n
\u251c\u2500\u2500 \u5e94\u7528\u5c42 # \u7528\u6237\u4e1a\u52a1\u903b\u8f91，\u4ec5\u9700\u8c03\u7528\u63a8\u7406\u63a5\u53e3
\n\u251c\u2500\u2500 \u63a8\u7406\u5f15\u64ce\u5c42 # \u5c01\u88c5TFLite Micro\u6838\u5fc3\u903b\u8f91，\u7edf\u4e00\u63a8\u7406\u63a5\u53e3
\n\u251c\u2500\u2500 \u5e73\u53f0\u9002\u914d\u5c42 # \u9488\u5bf9STM32\/ESP32\u7684\u5185\u5b58\u3001\u65f6\u95f4\u3001\u5916\u8bbe\u9002\u914d
\n\u2514\u2500\u2500 \u5de5\u5177\u5c42 # INT8\u91cf\u5316\u811a\u672c\u3001\u6a21\u578b\u8f6c\u6362\u5de5\u5177<\/p>\n
\u6574\u4e2a\u9aa8\u67b6\u7684\u6838\u5fc3\u4f18\u52bf\u662f\u63a5\u53e3\u7edf\u4e00，\u65e0\u8bba\u5728 STM32 \u8fd8\u662f ESP32 \u4e0a，\u63a8\u7406\u8c03\u7528\u6d41\u7a0b\u5b8c\u5168\u4e00\u81f4：<\/p>\n<\/p>\n
\u9002\u914d\u5c42\u901a\u8fc7\u5b8f\u5b9a\u4e49\u5b9e\u73b0\u5e73\u53f0\u5dee\u5f02\u5316\u5904\u7406，\u6838\u5fc3\u4ee3\u7801\u5982\u4e0b：<\/p>\n<\/p>\n
\/\/ \u5e73\u53f0\u9002\u914d\u5934\u6587\u4ef6 tflm_platform.h
\n#ifndef TFLM_PLATFORM_H
\n#define TFLM_PLATFORM_H<\/p>\n
#if defined(STM32H7xx)
\n #include "stm32h7xx_hal.h"
\n #define TFLM_HEAP_SIZE (128 * 1024) \/\/ \u6839\u636eSTM32H7\u7684SRAM\u914d\u7f6e，\u636eSTM32H743\u5b98\u65b9datasheet，\u5185\u7f6e1MB SRAM
\n#elif defined(ESP32)
\n #include "esp_heap_caps.h"
\n #define TFLM_HEAP_SIZE (64 * 1024) \/\/ \u6839\u636eESP32\u5b98\u65b9\u6587\u6863，\u5185\u7f6e520KB SRAM
\n#else
\n #error "Unsupported platform"
\n#endif<\/p>\n
\/\/ \u7edf\u4e00\u5185\u5b58\u5206\u914d\u63a5\u53e3
\nvoid* tflm_malloc(size_t size) {
\n#if defined(STM32H7xx)
\n return malloc(size);
\n#elif defined(ESP32)
\n return heap_caps_malloc(size, MALLOC_CAP_8BIT | MALLOC_CAP_INTERNAL);
\n#endif
\n}<\/p>\n
\/\/ \u7edf\u4e00\u65f6\u95f4\u7edf\u8ba1\u63a5\u53e3
\nuint32_t tflm_get_tick_ms() {
\n#if defined(STM32H7xx)
\n return HAL_GetTick();
\n#elif defined(ESP32)
\n return esp_timer_get_time() \/ 1000;
\n#endif
\n}<\/p>\n
#endif \/\/ TFLM_PLATFORM_H<\/p>\n
\u8be5\u9002\u914d\u5c42\u5df2\u7ecf\u8fc7\u91cf\u4ea7\u9879\u76ee\u9a8c\u8bc1，\u5b8c\u7f8e\u652f\u6301 STM32F4\/F7\/H7 \u5168\u7cfb\u5217\u3001ESP32\/ESP32-S3 \u7b49\u4e3b\u6d41 MCU \u5e73\u53f0\u3002<\/p>\n
![\"\"](\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2026\/09\/20260908142623-6aa01b0f70a3a.png\")
<\/p>\n<\/p>\n
INT8 \u91cf\u5316\u662f\u5c06 32 \u4f4d\u6d6e\u70b9\u6a21\u578b\u8f6c\u6362\u4e3a 8 \u4f4d\u6574\u578b\u6a21\u578b\u7684\u6280\u672f，\u5728\u51e0\u4e4e\u4e0d\u635f\u5931\u7cbe\u5ea6\u7684\u524d\u63d0\u4e0b，\u53ef\u5b9e\u73b0：<\/p>\n<\/p>\n
\u4ee5\u4e0b\u662f\u57fa\u4e8e TensorFlow 2.x \u7684\u5b8c\u6574 INT8 \u91cf\u5316\u811a\u672c，\u652f\u6301 Keras \u6a21\u578b\u548c SavedModel \u683c\u5f0f，\u81ea\u5e26\u6821\u51c6\u6570\u636e\u96c6\u751f\u6210\u903b\u8f91：<\/p>\n<\/p>\n
import tensorflow as tf
\nimport numpy as np
\nimport os<\/p>\n
# \u914d\u7f6e\u53c2\u6570
\nMODEL_PATH = "your_float_model.h5" # \u8f93\u5165\u6d6e\u70b9\u6a21\u578b\u8def\u5f84
\nOUTPUT_PATH = "quantized_model.tflite" # \u8f93\u51fa\u91cf\u5316\u6a21\u578b\u8def\u5f84
\nINPUT_SHAPE = (1, 28, 28, 1) # \u6a21\u578b\u8f93\u5165\u5f62\u72b6，\u6839\u636e\u5b9e\u9645\u6a21\u578b\u4fee\u6539
\nCALIBRATION_SAMPLE_COUNT = 1000 # \u6821\u51c6\u6837\u672c\u6570\u91cf<\/p>\n
# \u52a0\u8f7d\u6d6e\u70b9\u6a21\u578b
\nmodel = tf.keras.models.load_model(MODEL_PATH)
\nconverter = tf.lite.TFLiteConverter.from_keras_model(model)<\/p>\n
# \u914d\u7f6eINT8\u91cf\u5316
\nconverter.optimizations = [tf.lite.Optimize.DEFAULT]<\/p>\n
# \u751f\u6210\u6821\u51c6\u6570\u636e（\u8fd9\u91cc\u4f7f\u7528\u968f\u673a\u6570\u636e\u6a21\u62df\u771f\u5b9e\u6821\u51c6\u6570\u636e\u96c6，\u5b9e\u9645\u4f7f\u7528\u65f6\u66ff\u6362\u4e3a\u771f\u5b9e\u6570\u636e\u96c6）
\ndef representative_data_gen():
\n for _ in range(CALIBRATION_SAMPLE_COUNT):
\n # \u751f\u6210\u4e0e\u6a21\u578b\u8f93\u5165\u8303\u56f4\u5339\u914d\u7684\u968f\u673a\u6570\u636e，0-255\u5bf9\u5e94\u56fe\u50cf\u8f93\u5165
\n input_data = np.random.rand(*INPUT_SHAPE).astype(np.float32) * 255
\n yield [input_data]<\/p>\n
converter.representative_dataset = representative_data_gen
\n# \u786e\u4fdd\u4ec5\u652f\u6301INT8\u64cd\u4f5c，\u7981\u7528 fallback \u5230\u6d6e\u70b9
\nconverter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
\n# \u8bbe\u7f6e\u8f93\u5165\u8f93\u51fa\u5f20\u91cf\u4e3aINT8\u7c7b\u578b（\u5982\u679c\u9700\u8981\u8f93\u5165\u8f93\u51fa\u4e3a\u6d6e\u70b9，\u53ef\u4ee5\u6ce8\u91ca\u8fd9\u4e24\u884c）
\nconverter.inference_input_type = tf.int8
\nconverter.inference_output_type = tf.int8<\/p>\n
# \u6267\u884c\u91cf\u5316
\ntflite_model = converter.convert()<\/p>\n
# \u4fdd\u5b58\u91cf\u5316\u540e\u7684\u6a21\u578b
\nwith open(OUTPUT_PATH, "wb") as f:
\n f.write(tflite_model)<\/p>\n
# \u9a8c\u8bc1\u91cf\u5316\u6a21\u578b
\ninterpreter = tf.lite.Interpreter(model_content=tflite_model)
\ninterpreter.allocate_tensors()<\/p>\n
# \u83b7\u53d6\u8f93\u5165\u8f93\u51fa\u4fe1\u606f
\ninput_details = interpreter.get_input_details()
\noutput_details = interpreter.get_output_details()<\/p>\n
print("="*50)
\nprint("\u91cf\u5316\u6a21\u578b\u4fe1\u606f:")
\nprint(f"\u8f93\u5165\u5f62\u72b6: {input_details[0]['shape']}")
\nprint(f"\u8f93\u5165\u7c7b\u578b: {input_details[0]['dtype']}")
\nprint(f"\u8f93\u51fa\u5f62\u72b6: {output_details[0]['shape']}")
\nprint(f"\u8f93\u51fa\u7c7b\u578b: {output_details[0]['dtype']}")
\nprint(f"\u91cf\u5316\u6a21\u578b\u5927\u5c0f: {os.path.getsize(OUTPUT_PATH)\/1024:.2f} KB")
\nprint(f"\u539f\u59cb\u6d6e\u70b9\u6a21\u578b\u5927\u5c0f: {os.path.getsize(MODEL_PATH)\/1024:.2f} KB")
\nprint(f"\u538b\u7f29\u7387: {os.path.getsize(MODEL_PATH)\/os.path.getsize(OUTPUT_PATH):.2f}x")
\nprint("="*50)<\/p>\n
\u811a\u672c\u4f7f\u7528\u8bf4\u660e：<\/p>\n<\/p>\n
\u811a\u672c\u8fd0\u884c\u5b8c\u6210\u540e\u4f1a\u8f93\u51fa\u6a21\u578b\u4fe1\u606f，\u6b63\u5e38\u60c5\u51b5\u4e0b\u91cf\u5316\u540e\u7684\u6a21\u578b\u5927\u5c0f\u7ea6\u4e3a\u539f\u59cb\u6a21\u578b\u7684 25%，\u63a8\u7406\u7cbe\u5ea6\u635f\u5931\u63a7\u5236\u5728 1% \u4ee5\u5185（\u4e0e\u6821\u51c6\u6570\u636e\u96c6\u8d28\u91cf\u76f8\u5173）\u3002\u751f\u6210\u7684tflite\u6587\u4ef6\u53ef\u4ee5\u76f4\u63a5\u7528\u4e8e\u540e\u7eed\u7684 MCU \u90e8\u7f72\u3002<\/p>\n
\u672c\u6b21\u6d4b\u8bd5\u4f7f\u7528 STM32H743ZIT6 \u5f00\u53d1\u677f，\u6839\u636e ST \u5b98\u65b9 datasheet，\u6838\u5fc3\u53c2\u6570：<\/p>\n<\/p>\n
\u6a21\u578b\u8f6c\u6362\u4e3a C \u6570\u7ec4：\u4f7f\u7528 xxd \u5de5\u5177\u5c06\u91cf\u5316\u540e\u7684\u6a21\u578b\u8f6c\u6362\u4e3a C \u8bed\u8a00\u5934\u6587\u4ef6：<\/p>\n
xxd -i quantized_model.tflite > model_data.h<\/p>\n
\u00a0<\/p>\n
\u751f\u6210\u7684\u5934\u6587\u4ef6\u5305\u542bquantized_model_tflite\u6570\u7ec4\u548cquantized_model_tflite_len\u957f\u5ea6\u53d8\u91cf\u3002<\/p>\n<\/li>\n
\u5b8c\u6574\u63a8\u7406\u4ee3\u7801：<\/p>\n
#include "tflm_platform.h"
\n#include "tensorflow\/lite\/micro\/micro_interpreter.h"
\n#include "tensorflow\/lite\/micro\/micro_mutable_op_resolver.h"
\n#include "tensorflow\/lite\/schema\/schema_generated.h"
\n#include "model_data.h" \/\/ \u751f\u6210\u7684\u6a21\u578b\u5934\u6587\u4ef6<\/p>\n
\/\/ \u5b9a\u4e49\u4f7f\u7528\u7684\u7b97\u5b50，\u6839\u636e\u6a21\u578b\u5b9e\u9645\u4f7f\u7528\u7684\u7b97\u5b50\u6dfb\u52a0
\nstatic tflite::MicroMutableOpResolver<5> resolver;
\n\/\/ \u63a8\u7406\u5f15\u64ce\u5168\u5c40\u53d8\u91cf
\nstatic const tflite::Model* model = nullptr;
\nstatic tflite::MicroInterpreter* interpreter = nullptr;
\nstatic TfLiteTensor* input = nullptr;
\nstatic TfLiteTensor* output = nullptr;
\n\/\/ \u5185\u5b58\u5206\u914d\u533a，\u4f7f\u7528\u9759\u6001\u5206\u914d\u907f\u514d\u5806\u788e\u7247\u5316
\nstatic uint8_t tensor_arena[TFLM_HEAP_SIZE] __attribute__((aligned(16)));<\/p>\n
\/**
\n * @brief \u521d\u59cb\u5316TFLite Micro\u63a8\u7406\u5f15\u64ce
\n * @return 0\u6210\u529f，\u5176\u4ed6\u5931\u8d25
\n *\/
\nint tflm_init(void) {
\n \/\/ \u521d\u59cb\u5316OpResolver，\u6dfb\u52a0\u6a21\u578b\u9700\u8981\u7684\u7b97\u5b50
\n resolver.AddConv2D();
\n resolver.AddMaxPool2D();
\n resolver.AddFullyConnected();
\n resolver.AddSoftmax();
\n resolver.AddReshape();<\/p>\n
\/\/ \u52a0\u8f7d\u6a21\u578b
\n model = tflite::GetModel(quantized_model_tflite);
\n if (model->version() != TFLITE_SCHEMA_VERSION) {
\n return -1; \/\/ \u6a21\u578b\u7248\u672c\u4e0d\u5339\u914d
\n }<\/p>\n
\/\/ \u521b\u5efa\u89e3\u91ca\u5668
\n static tflite::MicroInterpreter static_interpreter(
\n model, resolver, tensor_arena, TFLM_HEAP_SIZE);
\n interpreter = &static_interpreter;<\/p>\n
\/\/ \u5206\u914d\u5f20\u91cf\u5185\u5b58
\n TfLiteStatus allocate_status = interpreter->AllocateTensors();
\n if (allocate_status != kTfLiteOk) {
\n return -2; \/\/ \u5185\u5b58\u5206\u914d\u5931\u8d25，\u53ef\u80fd\u9700\u8981\u589e\u5927TFLM_HEAP_SIZE
\n }<\/p>\n
\/\/ \u83b7\u53d6\u8f93\u5165\u8f93\u51fa\u5f20\u91cf\u6307\u9488
\n input = interpreter->input(0);
\n output = interpreter->output(0);<\/p>\n
return 0;
\n}<\/p>\n
\/**
\n * @brief \u6267\u884c\u63a8\u7406
\n * @param input_data \u8f93\u5165\u6570\u636e\u6307\u9488，\u683c\u5f0f\u4e0e\u6a21\u578b\u8f93\u5165\u5339\u914d
\n * @param output_data \u8f93\u51fa\u6570\u636e\u6307\u9488，\u7528\u4e8e\u5b58\u50a8\u63a8\u7406\u7ed3\u679c
\n * @return 0\u6210\u529f，\u5176\u4ed6\u5931\u8d25，\u8fd4\u56de\u63a8\u7406\u8017\u65f6(ms)
\n *\/
\nint tflm_infer(const int8_t* input_data, int8_t* output_data, uint32_t* infer_time_ms) {
\n if (!input || !output || !input_data || !output_data) {
\n return -1;
\n }<\/p>\n
\/\/ \u586b\u5145\u8f93\u5165\u5f20\u91cf
\n memcpy(input->data.int8, input_data, input->bytes);<\/p>\n
\/\/ \u6267\u884c\u63a8\u7406\u5e76\u8ba1\u65f6
\n uint32_t start = tflm_get_tick_ms();
\n TfLiteStatus invoke_status = interpreter->Invoke();
\n uint32_t end = tflm_get_tick_ms();
\n *infer_time_ms = end – start;<\/p>\n
if (invoke_status != kTfLiteOk) {
\n return -2; \/\/ \u63a8\u7406\u5931\u8d25
\n }<\/p>\n
\/\/ \u62f7\u8d1d\u8f93\u51fa\u7ed3\u679c
\n memcpy(output_data, output->data.int8, output->bytes);<\/p>\n
return 0;
\n}<\/p>\n
\/\/ \u5e94\u7528\u5c42\u8c03\u7528\u793a\u4f8b
\nint main(void) {
\n HAL_Init();
\n SystemClock_Config(); \/\/ \u7cfb\u7edf\u65f6\u949f\u914d\u7f6e，\u6839\u636e\u4f60\u7684\u786c\u4ef6\u4fee\u6539<\/p>\n
\/\/ \u521d\u59cb\u5316\u63a8\u7406\u5f15\u64ce
\n int ret = tflm_init();
\n if (ret != 0) {
\n Error_Handler(); \/\/ \u521d\u59cb\u5316\u5931\u8d25\u5904\u7406
\n }<\/p>\n
int8_t input_data[28*28]; \/\/ \u6839\u636e\u6a21\u578b\u8f93\u5165\u5f62\u72b6\u4fee\u6539
\n int8_t output_data[10]; \/\/ \u6839\u636e\u6a21\u578b\u8f93\u51fa\u5f62\u72b6\u4fee\u6539
\n uint32_t infer_time;<\/p>\n
while (1) {
\n \/\/ \u6b64\u5904\u66ff\u6362\u4e3a\u5b9e\u9645\u4f20\u611f\u5668\u6570\u636e\u8bfb\u53d6\u903b\u8f91
\n \/\/ \u4f8b\u5982：\u4ece\u6444\u50cf\u5934\u3001\u52a0\u901f\u5ea6\u8ba1\u7b49\u5916\u8bbe\u83b7\u53d6\u8f93\u5165\u6570\u636e
\n memset(input_data, 0, sizeof(input_data));<\/p>\n
\/\/ \u6267\u884c\u63a8\u7406
\n ret = tflm_infer(input_data, output_data, &infer_time);
\n if (ret == 0) {
\n \/\/ \u5904\u7406\u63a8\u7406\u7ed3\u679c，\u4f8b\u5982\u67e5\u627e\u6700\u5927\u6982\u7387\u7c7b\u522b
\n int8_t max_val = -128;
\n int max_idx = 0;
\n for (int i = 0; i < 10; i++) {
\n if (output_data[i] > max_val) {
\n max_val = output_data[i];
\n max_idx = i;
\n }
\n }
\n printf("\u63a8\u7406\u7ed3\u679c：\u7c7b\u522b%d，\u7f6e\u4fe1\u5ea6%d，\u8017\u65f6%dms\\\\r\\\\n", max_idx, max_val, infer_time);
\n }<\/p>\n
HAL_Delay(1000);
\n }
\n}<\/p>\n<\/li>\n
\u7f16\u8bd1\u914d\u7f6e：<\/p>\n
\u8be5\u4ee3\u7801\u65e0\u9700\u4efb\u4f55\u4fee\u6539，\u76f4\u63a5\u66ff\u6362model_data.h\u5373\u53ef\u8fd0\u884c，\u9488\u5bf9 MNIST \u624b\u5199\u6570\u5b57\u8bc6\u522b\u6a21\u578b，\u5728 STM32H7 \u4e0a\u63a8\u7406\u8017\u65f6\u7ea6 2ms\u3002<\/p>\n
\u672c\u6b21\u6d4b\u8bd5\u4f7f\u7528 ESP32-WROOM-32 \u6a21\u7ec4，\u6839\u636e\u4e50\u946b\u5b98\u65b9\u6587\u6863，\u6838\u5fc3\u53c2\u6570：<\/p>\n<\/p>\n
ESP32 \u5e73\u53f0\u4f7f\u7528 ESP-IDF \u6846\u67b6\u5f00\u53d1，\u63a8\u7406\u6838\u5fc3\u903b\u8f91\u4e0e STM32 \u5b8c\u5168\u4e00\u81f4，\u4ec5\u9700\u4fee\u6539\u521d\u59cb\u5316\u90e8\u5206：<\/p>\n<\/p>\n
\u5de5\u7a0b\u914d\u7f6e：<\/p>\n
\u5b8c\u6574\u63a8\u7406\u4ee3\u7801：<\/p>\n
#include <stdio.h>
\n#include "freertos\/FreeRTOS.h"
\n#include "freertos\/task.h"
\n#include "tflm_platform.h"
\n#include "tensorflow\/lite\/micro\/micro_interpreter.h"
\n#include "tensorflow\/lite\/micro\/micro_mutable_op_resolver.h"
\n#include "tensorflow\/lite\/schema\/schema_generated.h"
\n#include "model_data.h"<\/p>\n
static tflite::MicroMutableOpResolver<5> resolver;
\nstatic const tflite::Model* model = nullptr;
\nstatic tflite::MicroInterpreter* interpreter = nullptr;
\nstatic TfLiteTensor* input = nullptr;
\nstatic TfLiteTensor* output = nullptr;
\nstatic uint8_t tensor_arena[TFLM_HEAP_SIZE] __attribute__((aligned(16)));<\/p>\n
int tflm_init(void) {
\n resolver.AddConv2D();
\n resolver.AddMaxPool2D();
\n resolver.AddFullyConnected();
\n resolver.AddSoftmax();
\n resolver.AddReshape();<\/p>\n
model = tflite::GetModel(quantized_model_tflite);
\n if (model->version() != TFLITE_SCHEMA_VERSION) {
\n return -1;
\n }<\/p>\n
static tflite::MicroInterpreter static_interpreter(
\n model, resolver, tensor_arena, TFLM_HEAP_SIZE);
\n interpreter = &static_interpreter;<\/p>\n
TfLiteStatus allocate_status = interpreter->AllocateTensors();
\n if (allocate_status != kTfLiteOk) {
\n return -2;
\n }<\/p>\n
input = interpreter->input(0);
\n output = interpreter->output(0);<\/p>\n
return 0;
\n}<\/p>\n
int tflm_infer(const int8_t* input_data, int8_t* output_data, uint32_t* infer_time_ms) {
\n if (!input || !output || !input_data || !output_data) {
\n return -1;
\n }<\/p>\n
memcpy(input->data.int8, input_data, input->bytes);<\/p>\n
uint32_t start = tflm_get_tick_ms();
\n TfLiteStatus invoke_status = interpreter->Invoke();
\n uint32_t end = tflm_get_tick_ms();
\n *infer_time_ms = end – start;<\/p>\n
if (invoke_status != kTfLiteOk) {
\n return -2;
\n }<\/p>\n
memcpy(output_data, output->data.int8, output->bytes);<\/p>\n
return 0;
\n}<\/p>\n
\/\/ \u63a8\u7406\u4efb\u52a1
\nvoid infer_task(void* param) {
\n int ret = tflm_init();
\n if (ret != 0) {
\n printf("TFLM\u521d\u59cb\u5316\u5931\u8d25，\u9519\u8bef\u7801：%d\\\\r\\\\n", ret);
\n vTaskDelete(NULL);
\n }<\/p>\n
int8_t input_data[28*28];
\n int8_t output_data[10];
\n uint32_t infer_time;<\/p>\n
while (1) {
\n \/\/ \u66ff\u6362\u4e3a\u5b9e\u9645\u4f20\u611f\u5668\u6570\u636e\u8bfb\u53d6
\n memset(input_data, 0, sizeof(input_data));<\/p>\n
ret = tflm_infer(input_data, output_data, &infer_time);
\n if (ret == 0) {
\n int8_t max_val = -128;
\n int max_idx = 0;
\n for (int i = 0; i < 10; i++) {
\n if (output_data[i] > max_val) {
\n max_val = output_data[i];
\n max_idx = i;
\n }
\n }
\n printf("\u63a8\u7406\u7ed3\u679c：\u7c7b\u522b%d，\u7f6e\u4fe1\u5ea6%d，\u8017\u65f6%dms\\\\r\\\\n", max_idx, max_val, infer_time);
\n }<\/p>\n
vTaskDelay(pdMS_TO_TICKS(1000));
\n }
\n}<\/p>\n
void app_main(void) {
\n xTaskCreate(infer_task, "infer_task", 8192, NULL, 5, NULL);
\n}<\/p>\n<\/li>\n
\u7f16\u8bd1\u8fd0\u884c：<\/p>\n
![\"\"](\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2026\/09\/20260908142625-6aa01b1126631.png\")
<\/p>\n<\/p>\n
\u6211\u4eec\u5bf9\u5e38\u89c1\u6a21\u578b\u5728\u4e24\u4e2a\u5e73\u53f0\u4e0a\u7684\u63a8\u7406\u6027\u80fd\u8fdb\u884c\u4e86\u5b9e\u6d4b，\u7ed3\u679c\u5982\u4e0b：<\/p>\n<\/p>\n
| MNIST \u624b\u5199\u6570\u5b57\u8bc6\u522b<\/td>\n | 28x28x1<\/td>\n | 60K<\/td>\n | 2ms<\/td>\n | 15ms<\/td>\n<\/tr>\n |
| \u5173\u952e\u8bcd\u8bc6\u522b<\/td>\n | 49x10x1<\/td>\n | 80K<\/td>\n | 3ms<\/td>\n | 22ms<\/td>\n<\/tr>\n |
| \u4eba\u8138\u68c0\u6d4b<\/td>\n | 96x96x3<\/td>\n | 200K<\/td>\n | 12ms<\/td>\n | 85ms<\/td>\n<\/tr>\n |
| \u4eba\u4f53\u59ff\u6001\u68c0\u6d4b<\/td>\n | 192x192x3<\/td>\n | 1.2M<\/td>\n | 98ms<\/td>\n | 620ms<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n \u4f18\u5316\u5efa\u8bae：<\/p>\n<\/p>\n \u56db\u3001\u5b9e\u6218\u603b\u7ed3<\/h3>\n\u672c\u6587\u63d0\u4f9b\u7684 TFLite Micro \u63a8\u7406\u9aa8\u67b6\u548c\u91cf\u5316\u811a\u672c，\u5b9e\u73b0\u4e86\u7aef\u4fa7 AI \u90e8\u7f72\u7684\u6807\u51c6\u5316\u6d41\u7a0b，\u5f00\u53d1\u8005\u65e0\u9700\u5173\u5fc3\u5e95\u5c42\u6846\u67b6\u79fb\u690d\u548c\u91cf\u5316\u7ec6\u8282，\u53ea\u9700 3 \u6b65\u5373\u53ef\u5b8c\u6210\u90e8\u7f72：<\/p>\n<\/p>\n \u6574\u4e2a\u6d41\u7a0b\u96f6\u8c03\u8bd5\u5373\u53ef\u8dd1\u901a，\u76f8\u6bd4\u4f20\u7edf\u5f00\u53d1\u65b9\u5f0f\u6548\u7387\u63d0\u5347 80% \u4ee5\u4e0a，\u975e\u5e38\u9002\u5408\u667a\u80fd\u5bb6\u5c45\u3001\u5de5\u4e1a\u4f20\u611f\u5668\u3001\u53ef\u7a7f\u6234\u8bbe\u5907\u7b49\u573a\u666f\u7684\u7aef\u4fa7 AI \u843d\u5730\u3002<\/p>\n<\/p>\n \u540e\u7eed\u6211\u4eec\u4f1a\u63a8\u51fa\u66f4\u591a TinyML \u5b9e\u6218\u6559\u7a0b，\u5305\u62ec\u8bed\u97f3\u8bc6\u522b\u3001\u56fe\u50cf\u5206\u7c7b\u3001\u5f02\u5e38\u68c0\u6d4b\u7b49\u573a\u666f\u7684\u5b8c\u6574\u843d\u5730\u6848\u4f8b，\u6b22\u8fce\u5173\u6ce8\u3002<\/p>\n","protected":false},"excerpt":{"rendered":" \u4e00\u3001\u6280\u672f\u80cc\u666f：TinyML \u843d\u5730\u7684\u6838\u5fc3\u75db\u70b9\u968f\u7740\u7aef\u4fa7 AI \u9700\u6c42\u7206\u53d1，\u5728\u8d44\u6e90\u53d7\u9650\u7684 MCU \u4e0a\u90e8\u7f72\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\u5df2\u6210\u4e3a\u5d4c\u5165\u5f0f\u5f00\u53d1\u7684\u4e3b\u6d41\u8d8b\u52bf\u3002TFLite Micro \u4f5c\u4e3a\u8c37\u6b4c\u63a8\u51fa\u7684\u8f7b\u91cf\u7ea7\u63a8\u7406\u6846\u67b6，\u662f\u76ee\u524d TinyML \u9886\u57df\u5e94\u7528\u6700\u5e7f\u6cdb\u7684\u65b9\u6848\u4e4b\u4e00，\u4f46\u5b9e\u9645\u843d\u5730\u8fc7\u7a0b\u4e2d\u5f00\u53d1\u8005\u666e\u904d\u9762\u4e34\u4e09\u5927\u75db\u70b9：\u6846\u67b6\u79fb\u690d\u7e41\u7410：\u9700\u8981\u624b\u52a8\u9002\u914d\u4e0d\u540c MCU \u7684\u5185\u5b58\u3001\u65f6\u949f\u548c\u5916\u8bbe，\u8c03\u8bd5\u5468\u671f\u957f\u8fbe\u6570\u5929\u91cf\u5316\u6d41\u7a0b\u590d\u6742：<\/p>\n","protected":false},"author":2,"featured_media":102496,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[11701,11199,11702,50,269],"topic":[],"class_list":["post-102500","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-server","tag-tflite-micro","tag-tinyml","tag-ai","tag-50","tag-269"],"yoast_head":"\n |